I was sitting in a conference in August, drifting as I so often do between listening to what was on stage and pretending to be doing important work on my laptop (by which I mean sending memes on WhatsApp).

All of a sudden, from the stage, the renowned biologist came out with a sentence that made every single person in the room sit up and pay attention: 鈥淥f course,鈥� she said, 鈥渨e now know that 础濒锄丑别颈尘别谤鈥檚 starts in the nose.鈥�

I鈥檓 sorry, what? Starts in the nose? I didn鈥檛 know 础濒锄丑别颈尘别谤鈥檚 starts in the nose.

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My own lovely dad died from 础濒锄丑别颈尘别谤鈥檚 last year, and ever since his diagnosis, I鈥檝e done an incredible amount of digging into the disease, trying to understand what was happening to him. I was astonished I鈥檇 never come across this idea.

And perhaps you haven鈥檛 either. Which is why I want to share my astonishment at discovering that Aviv Regev was right 鈥� not only is 础濒锄丑别颈尘别谤鈥檚 closely associated with our sense of smell, but there is a growing body of research suggesting that our noses might have something to do with why the disease develops in the first place.

The first bit you might already know: people with 础濒锄丑别颈尘别谤鈥檚 tend to lose their sense of smell, sometimes years before any symptoms of memory loss start to show up. But it turns out that our ability to smell is a surprisingly good indicator of our overall health (looking at you, covid-19). One study from 2014 found that of whether they would be dead within five years than a cancer diagnosis.

It鈥檚 not that losing your sense of smell itself will kill you. More that the olfactory nerves are deeply intertwined with some of the most important memory structures in the brain, so when they start to deteriorate, it can be an early warning that something much more serious is going on.

And this connection seems to work in both directions. One intriguing study (admittedly with quite a small sample size) , thus exposing them to all kinds of wonderful scents while they slept. After six months, the people who had the olfactory enrichment performed better on memory tests than those in a control group.

All very interesting. But not what Regev had been suggesting. Her words seemed to imply that noses are actually responsible for the trouble in the first place.

It turns out that the reason scientists are even entertaining this possibility is because of how fiercely our brains are protected from the outside world. Our skulls are like an actual fortress: even our blood isn鈥檛 allowed in without passing a particularly fussy bouncer called the blood-brain barrier.

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One of the only real exceptions is a little bit of bone right at the top of your nose that is riddled with holes. It鈥檚 called the cribriform, and it鈥檚 where the olfactory fibres dangle down into your nose to give you your sense of smell 鈥� a bit like if you pushed some Play-Doh through a sieve.

And that means that, despite all the elaborate defences protecting your brain, there is a potential route in, sitting right in the middle of your face.

So, here鈥檚 the idea. Maybe the beta-amyloid plaques, the sticky little protein fragments that appear to play such a key role in the cascade of 础濒锄丑别颈尘别谤鈥檚 in the brain, are what鈥檚 left over after the brain has fought an infection. And maybe the nose is how the infection arrives.

To see if this held any water, in 2022, a group of scientists of some mice.

(I should add, for the sake of the mice鈥檚 reputation, that it was actually Chlamydia pneumoniae, a cousin of the bacterium that causes the STI. But I hope you鈥檒l forgive me the less scientifically accurate version. In a long career, you get very few chances to write sentences that good, and I wasn鈥檛 about to waste mine.)

The remarkable thing, though, was that within 72 hours, the bacteria had travelled up the nerves in the nasal cavity and got through to the brain. What鈥檚 more, the infection triggered the accumulation of beta-amyloid. Now that鈥檚 not definitive proof of how 础濒锄丑别颈尘别谤鈥檚 starts in humans. But there is some other, rather unsettling evidence that infections aren鈥檛 the only unwelcome visitors that manage to get in that way.

A group in Mexico . Inside the dogs鈥� brains, they found microscopic particles of metals 鈥� that seem to have entered through the nose 鈥� wrapped in pockets of inflammation and that same beta-amyloid protein. You don鈥檛 see the same thing in dogs that lived in cleaner, more rural environments.

Now these studies have been extended to who died from gun violence or car accidents 鈥� and they find something similar. Tiny, metal鈥搑ich particles, thought to originate from exhausts and brake dust, turned up in their brain tissue, alongside inflammation and accumulations of beta-amyloid. The very sorts of changes associated with 础濒锄丑别颈尘别谤鈥檚, appearing in brains that were sometimes only a few years old. Put together, it implies that the disease might have an environmental cause, one that begins much much earlier than we thought.

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So, where does 础濒锄丑别颈尘别谤鈥檚 start? The answer is no one nose (pun very much intended). There is still a great deal we don鈥檛 understand, but the implications from all this research are rather extraordinary. What if the beta-amyloid plaques we鈥檝e spent so long trying to get rid of are, at least sometimes, the aftermath of the brain trying to protect itself? And what if, rather than focusing entirely on clearing up the damage, we could work out what set it off in the first place?

But more than anything, I can鈥檛 help but laugh as I think about all this in the context of my dad, who had many wonderful qualities, but a discreet nose wasn鈥檛 one of them. We used to say that his nose would enter a room 15 minutes before he did. He would have found it very funny indeed if it turned out to have been the cause of his downfall.

Hannah Fry: How OpenAI has caused an existential crisis in mathematics

OpenAI has been throwing everything at solving long-standing maths problems. This should have been a glorious moment for mathematicians 鈥� but instead we're having a massive collective wobble, says New Scientist's new columnist, Professor Hannah Fry

A few years ago, when I first started talking about mathematics in public, a professor of algebraic number theory pulled me to one side and gave me some sharp words of warning: 鈥淭he world fears mathematicians, Hannah. Let鈥檚 keep it that way.鈥�

He was being, to put it mildly, a bit of a snob. Maths had an intimidating mystique to maintain, and I was apparently jeopardising it by titting around on the internet making videos and podcasts, rather than focusing on a perfectly respectable research career.

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I have thought a lot about that comment since. Partly because I have spent the intervening years doing everything I can to completely ignore him (and getting a New Scientist column feels like a rather lovely reward for doing so).

But buried beneath his snobbery, I have come around to the idea that he was right about something. And I have never felt that more than in the past few weeks, since a swarm of AI agents have been busy solving our most famous long-standing problems.

I鈥檓 talking, of course, about OpenAI鈥檚 announcements over the past few weeks that it has found proofs for hundreds of mathematics problems that had previously resisted human attempts to solve them.

The furore began, most notably, with a proof (by way of finding a counterexample) that the Navier-Stokes equations can break apart. This particular gem was such a big deal to the mathematical community that at the turn of the millennium, the Clay Mathematics Institute promised they鈥檇 give $1 million to whoever managed to solve it.

Those Navier-Stokes equations are what I did my PhD on. They are basically Isaac Newton鈥檚 second law of motion: F=ma, but applied to fluids. They are, in my not entirely unbiased opinion, staggeringly beautiful, able to articulate everything from the drip of a water droplet on a leaf and the churn of an entire atmosphere to the gases swirling around in a galaxy in the sky.

The thing is, these equations have a tricky little bit that pops out of the acceleration part, which contains a feedback loop within it. The faster fluid flows, the faster the gradient can get between two adjacent streams, and so the faster fluid flows. It鈥檚 a bit like the fluid version of putting a microphone next to a speaker. The feedback loop can get louder and louder until everything ends up breaking. Or, in the case of the Navier-Stokes equations, the speed of the flow heads off to infinity.

To be clear: no one was ever worried about fluid dynamics tearing open a black hole when you stir your tea or anything. It was just that the weird behaviour of this little term left the possibility that the equations themselves would break.

On the flip side, there鈥檚 another term in the equation 鈥� the viscosity 鈥� that acts as a brake and calms everything down, stopping the flow from getting completely out of control. So, the question was: does the brake always kick in?

If you have been following the story, then by now you will know what happened. Various groups of mathematicians had been using AI to circle in on this problem, finding special cases where the feedback loop goes crazy, without quite getting to the full Navier-Stokes.

But supposedly, once OpenAI heard that others were getting close, they threw everything at it: 10,000 AI agents, sending each other 3 million messages over 88 hours, costing millions of dollars in tokens, to find a single example where everything blows up. The Navier-Stokes equations do indeed fall. And an AI proved it.

This should have been a glorious moment of scientific exposition, but instead, mathematicians have spent the last few weeks having a massive collective wobble.

Part of the reason is that nobody yet understands the answer. The OpenAI solution is almost hilariously ugly. It is a proof that鈥檚 166 pages long, accompanied by hundreds of thousands of lines of formal code 鈥� almost impenetrable symbols 鈥� slapped down on the table and left for the mathematicians to pick apart, while the AI labs move on to the next thing. They came for the glory without giving much back.

So, we are left with a proof that almost no one wants to read, a $1 million prize that no one will want to , and no one who can stand at a blackboard and explain why this particular vortex makes everything break, when nothing else we tried ever did.

And now, to top it off, the new tranche of proofs that landed last week claim to have polished off hundreds of other important, unsolved mathematical problems, including a big result in the biggest trophy in all of mathematics: the Riemann Hypothesis. All tipped onto the code-sharing website GitHub for someone else to check.

So, mathematicians are having their own existential crisis about their role and purpose, like Lee Sedol did after AlphaGo, Gary Kasparov before him with Deep Blue and probably every literature graduate, musician and artist since ChatGPT was launched.

Please don鈥檛 get me wrong. I want to use this column, at least in part, to talk about the incredible, world-changing things that are happening with AI in science. I know that there is some inevitability to machines outpacing the humans, and I know that at its best, AI has a great deal to offer.

But at its worst, AI is an engine of extraction. Not just text, or images or equations. The real sting in the tail is that OpenAI set its sights on mathematics because the prestige, the respect and the glory was just another thing that could be mined.

It didn鈥檛 need to be that way.

They so easily could have been open and collaborative. But the moment when one of humanity鈥檚 greatest intellectual traditions became vulnerable, they chose conquest over stewardship.

And so, I am left thinking about that professor again. He was right that there is a special mystique to mathematics, but wrong about who would puncture it. And I鈥檓 sorry to say that, while the world might fear mathematicians, the machines certainly don鈥檛.